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data.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Author: Yue Wang
@Contact: [email protected]
@File: data.py
@Time: 2018/10/13 6:21 PM
Modified by
@Author: An Tao, Pengliang Ji
@Contact: [email protected], [email protected]
@Time: 2021/7/20 7:49 PM
"""
import os
import sys
import glob
import h5py
import numpy as np
import torch
import json
import cv2
from torch.utils.data import Dataset
def download_modelnet40():
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
DATA_DIR = os.path.join(BASE_DIR, 'data')
if not os.path.exists(DATA_DIR):
os.mkdir(DATA_DIR)
if not os.path.exists(os.path.join(DATA_DIR, 'modelnet40_ply_hdf5_2048')):
www = 'https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip'
zipfile = os.path.basename(www)
os.system('wget %s --no-check-certificate; unzip %s' % (www, zipfile))
os.system('mv %s %s' % ('modelnet40_ply_hdf5_2048', DATA_DIR))
os.system('rm %s' % (zipfile))
def download_shapenetpart():
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
DATA_DIR = os.path.join(BASE_DIR, 'data')
if not os.path.exists(DATA_DIR):
os.mkdir(DATA_DIR)
if not os.path.exists(os.path.join(DATA_DIR, 'shapenet_part_seg_hdf5_data')):
www = 'https://shapenet.cs.stanford.edu/media/shapenet_part_seg_hdf5_data.zip'
zipfile = os.path.basename(www)
os.system('wget %s --no-check-certificate; unzip %s' % (www, zipfile))
os.system('mv %s %s' % ('hdf5_data', os.path.join(DATA_DIR, 'shapenet_part_seg_hdf5_data')))
os.system('rm %s' % (zipfile))
def download_S3DIS():
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
DATA_DIR = os.path.join(BASE_DIR, 'data')
if not os.path.exists(DATA_DIR):
os.mkdir(DATA_DIR)
if not os.path.exists(os.path.join(DATA_DIR, 'indoor3d_sem_seg_hdf5_data')):
www = 'https://shapenet.cs.stanford.edu/media/indoor3d_sem_seg_hdf5_data.zip'
zipfile = os.path.basename(www)
os.system('wget %s --no-check-certificate; unzip %s' % (www, zipfile))
os.system('mv %s %s' % ('indoor3d_sem_seg_hdf5_data', DATA_DIR))
os.system('rm %s' % (zipfile))
if not os.path.exists(os.path.join(DATA_DIR, 'Stanford3dDataset_v1.2_Aligned_Version')):
if not os.path.exists(os.path.join(DATA_DIR, 'Stanford3dDataset_v1.2_Aligned_Version.zip')):
print('Please download Stanford3dDataset_v1.2_Aligned_Version.zip \
from https://goo.gl/forms/4SoGp4KtH1jfRqEj2 and place it under data/')
sys.exit(0)
else:
zippath = os.path.join(DATA_DIR, 'Stanford3dDataset_v1.2_Aligned_Version.zip')
os.system('unzip %s' % (zippath))
os.system('mv %s %s' % ('Stanford3dDataset_v1.2_Aligned_Version', DATA_DIR))
os.system('rm %s' % (zippath))
def load_data_cls(partition):
download_modelnet40()
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
DATA_DIR = os.path.join(BASE_DIR, 'data')
all_data = []
all_label = []
for h5_name in glob.glob(os.path.join(DATA_DIR, 'modelnet40_ply_hdf5_2048', '*%s*.h5'%partition)):
f = h5py.File(h5_name, 'r+')
data = f['data'][:].astype('float32')
label = f['label'][:].astype('int64')
f.close()
all_data.append(data)
all_label.append(label)
all_data = np.concatenate(all_data, axis=0)
all_label = np.concatenate(all_label, axis=0)
return all_data, all_label
def load_data_partseg(partition):
download_shapenetpart()
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
DATA_DIR = os.path.join(BASE_DIR, 'data')
all_data = []
all_label = []
all_seg = []
if partition == 'trainval':
file = glob.glob(os.path.join(DATA_DIR, 'shapenet_part_seg_hdf5_data', '*train*.h5')) \
+ glob.glob(os.path.join(DATA_DIR, 'shapenet_part_seg_hdf5_data', '*val*.h5'))
else:
file = glob.glob(os.path.join(DATA_DIR, 'shapenet_part_seg_hdf5_data', '*%s*.h5'%partition))
for h5_name in file:
f = h5py.File(h5_name, 'r+')
data = f['data'][:].astype('float32')
label = f['label'][:].astype('int64')
seg = f['pid'][:].astype('int64')
f.close()
all_data.append(data)
all_label.append(label)
all_seg.append(seg)
all_data = np.concatenate(all_data, axis=0)
all_label = np.concatenate(all_label, axis=0)
all_seg = np.concatenate(all_seg, axis=0)
return all_data, all_label, all_seg
def prepare_test_data_semseg():
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
DATA_DIR = os.path.join(BASE_DIR, 'data')
if not os.path.exists(os.path.join(DATA_DIR, 'stanford_indoor3d')):
os.system('python prepare_data/collect_indoor3d_data.py')
if not os.path.exists(os.path.join(DATA_DIR, 'indoor3d_sem_seg_hdf5_data_test')):
os.system('python prepare_data/gen_indoor3d_h5.py')
def load_data_semseg(partition, test_area):
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
DATA_DIR = os.path.join(BASE_DIR, 'data')
download_S3DIS()
prepare_test_data_semseg()
if partition == 'train':
data_dir = os.path.join(DATA_DIR, 'indoor3d_sem_seg_hdf5_data')
else:
data_dir = os.path.join(DATA_DIR, 'indoor3d_sem_seg_hdf5_data_test')
with open(os.path.join(data_dir, "all_files.txt")) as f:
all_files = [line.rstrip() for line in f]
with open(os.path.join(data_dir, "room_filelist.txt")) as f:
room_filelist = [line.rstrip() for line in f]
data_batchlist, label_batchlist = [], []
for f in all_files:
file = h5py.File(os.path.join(DATA_DIR, f), 'r+')
data = file["data"][:]
label = file["label"][:]
data_batchlist.append(data)
label_batchlist.append(label)
data_batches = np.concatenate(data_batchlist, 0)
seg_batches = np.concatenate(label_batchlist, 0)
test_area_name = "Area_" + test_area
train_idxs, test_idxs = [], []
for i, room_name in enumerate(room_filelist):
if test_area_name in room_name:
test_idxs.append(i)
else:
train_idxs.append(i)
if partition == 'train':
all_data = data_batches[train_idxs, ...]
all_seg = seg_batches[train_idxs, ...]
else:
all_data = data_batches[test_idxs, ...]
all_seg = seg_batches[test_idxs, ...]
return all_data, all_seg
def load_color_partseg():
colors = []
labels = []
f = open("prepare_data/meta/partseg_colors.txt")
for line in json.load(f):
colors.append(line['color'])
labels.append(line['label'])
partseg_colors = np.array(colors)
partseg_colors = partseg_colors[:, [2, 1, 0]]
partseg_labels = np.array(labels)
font = cv2.FONT_HERSHEY_SIMPLEX
img_size = 1350
img = np.zeros((1350, 1890, 3), dtype="uint8")
cv2.rectangle(img, (0, 0), (1900, 1900), [255, 255, 255], thickness=-1)
column_numbers = [4, 2, 2, 4, 4, 3, 3, 2, 4, 2, 6, 2, 3, 3, 3, 3]
column_gaps = [320, 320, 300, 300, 285, 285]
color_size = 64
color_index = 0
label_index = 0
row_index = 16
for row in range(0, img_size):
column_index = 32
for column in range(0, img_size):
color = partseg_colors[color_index]
label = partseg_labels[label_index]
length = len(str(label))
cv2.rectangle(img, (column_index, row_index), (column_index + color_size, row_index + color_size),
color=(int(color[0]), int(color[1]), int(color[2])), thickness=-1)
img = cv2.putText(img, label, (column_index + int(color_size * 1.15), row_index + int(color_size / 2)),
font,
0.76, (0, 0, 0), 2)
column_index = column_index + column_gaps[column]
color_index = color_index + 1
label_index = label_index + 1
if color_index >= 50:
cv2.imwrite("prepare_data/meta/partseg_colors.png", img, [cv2.IMWRITE_PNG_COMPRESSION, 0])
return np.array(colors)
elif (column + 1 >= column_numbers[row]):
break
row_index = row_index + int(color_size * 1.3)
if (row_index >= img_size):
break
def load_color_semseg():
colors = []
labels = []
f = open("prepare_data/meta/semseg_colors.txt")
for line in json.load(f):
colors.append(line['color'])
labels.append(line['label'])
semseg_colors = np.array(colors)
semseg_colors = semseg_colors[:, [2, 1, 0]]
partseg_labels = np.array(labels)
font = cv2.FONT_HERSHEY_SIMPLEX
img_size = 1500
img = np.zeros((500, img_size, 3), dtype="uint8")
cv2.rectangle(img, (0, 0), (img_size, 750), [255, 255, 255], thickness=-1)
color_size = 64
color_index = 0
label_index = 0
row_index = 16
for _ in range(0, img_size):
column_index = 32
for _ in range(0, img_size):
color = semseg_colors[color_index]
label = partseg_labels[label_index]
length = len(str(label))
cv2.rectangle(img, (column_index, row_index), (column_index + color_size, row_index + color_size),
color=(int(color[0]), int(color[1]), int(color[2])), thickness=-1)
img = cv2.putText(img, label, (column_index + int(color_size * 1.15), row_index + int(color_size / 2)),
font,
0.7, (0, 0, 0), 2)
column_index = column_index + 200
color_index = color_index + 1
label_index = label_index + 1
if color_index >= 13:
cv2.imwrite("prepare_data/meta/semseg_colors.png", img, [cv2.IMWRITE_PNG_COMPRESSION, 0])
return np.array(colors)
elif (column_index >= 1280):
break
row_index = row_index + int(color_size * 1.3)
if (row_index >= img_size):
break
def translate_pointcloud(pointcloud):
xyz1 = np.random.uniform(low=2./3., high=3./2., size=[3])
xyz2 = np.random.uniform(low=-0.2, high=0.2, size=[3])
translated_pointcloud = np.add(np.multiply(pointcloud, xyz1), xyz2).astype('float32')
return translated_pointcloud
def jitter_pointcloud(pointcloud, sigma=0.01, clip=0.02):
N, C = pointcloud.shape
pointcloud += np.clip(sigma * np.random.randn(N, C), -1*clip, clip)
return pointcloud
def rotate_pointcloud(pointcloud):
theta = np.pi*2 * np.random.uniform()
rotation_matrix = np.array([[np.cos(theta), -np.sin(theta)],[np.sin(theta), np.cos(theta)]])
pointcloud[:,[0,2]] = pointcloud[:,[0,2]].dot(rotation_matrix) # random rotation (x,z)
return pointcloud
class ModelNet40(Dataset):
def __init__(self, num_points, partition='train'):
self.data, self.label = load_data_cls(partition)
self.num_points = num_points
self.partition = partition
def __getitem__(self, item):
pointcloud = self.data[item][:self.num_points]
label = self.label[item]
if self.partition == 'train':
pointcloud = translate_pointcloud(pointcloud)
np.random.shuffle(pointcloud)
return pointcloud, label
def __len__(self):
return self.data.shape[0]
class ShapeNetPart(Dataset):
def __init__(self, num_points, partition='train', class_choice=None):
self.data, self.label, self.seg = load_data_partseg(partition)
self.cat2id = {'airplane': 0, 'bag': 1, 'cap': 2, 'car': 3, 'chair': 4,
'earphone': 5, 'guitar': 6, 'knife': 7, 'lamp': 8, 'laptop': 9,
'motor': 10, 'mug': 11, 'pistol': 12, 'rocket': 13, 'skateboard': 14, 'table': 15}
self.seg_num = [4, 2, 2, 4, 4, 3, 3, 2, 4, 2, 6, 2, 3, 3, 3, 3]
self.index_start = [0, 4, 6, 8, 12, 16, 19, 22, 24, 28, 30, 36, 38, 41, 44, 47]
self.num_points = num_points
self.partition = partition
self.class_choice = class_choice
self.partseg_colors = load_color_partseg()
if self.class_choice != None:
id_choice = self.cat2id[self.class_choice]
indices = (self.label == id_choice).squeeze()
self.data = self.data[indices]
self.label = self.label[indices]
self.seg = self.seg[indices]
self.seg_num_all = self.seg_num[id_choice]
self.seg_start_index = self.index_start[id_choice]
else:
self.seg_num_all = 50
self.seg_start_index = 0
def __getitem__(self, item):
pointcloud = self.data[item][:self.num_points]
label = self.label[item]
seg = self.seg[item][:self.num_points]
if self.partition == 'trainval':
# pointcloud = translate_pointcloud(pointcloud)
indices = list(range(pointcloud.shape[0]))
np.random.shuffle(indices)
pointcloud = pointcloud[indices]
seg = seg[indices]
return pointcloud, label, seg
def __len__(self):
return self.data.shape[0]
class S3DIS(Dataset):
def __init__(self, num_points=4096, partition='train', test_area='1'):
self.data, self.seg = load_data_semseg(partition, test_area)
self.num_points = num_points
self.partition = partition
self.semseg_colors = load_color_semseg()
def __getitem__(self, item):
pointcloud = self.data[item][:self.num_points]
seg = self.seg[item][:self.num_points]
if self.partition == 'train':
indices = list(range(pointcloud.shape[0]))
np.random.shuffle(indices)
pointcloud = pointcloud[indices]
seg = seg[indices]
seg = torch.LongTensor(seg)
return pointcloud, seg
def __len__(self):
return self.data.shape[0]
if __name__ == '__main__':
train = ModelNet40(1024)
test = ModelNet40(1024, 'test')
data, label = train[0]
print(data.shape)
print(label.shape)
trainval = ShapeNetPart(2048, 'trainval')
test = ShapeNetPart(2048, 'test')
data, label, seg = trainval[0]
print(data.shape)
print(label.shape)
print(seg.shape)
train = S3DIS(4096)
test = S3DIS(4096, 'test')
data, seg = train[0]
print(data.shape)
print(seg.shape)